Authors:
Toon De Pessemier
;
Kris Vanhecke
;
Simon Dooms
;
Tom Deryckere
and
Luc Martens
Affiliation:
IBBT, Ghent University, Belgium
Keyword(s):
Recommender system, Personalization, Collaborative filtering, Profile, User-generated Content, Algorithm.
Abstract:
The enormous offer of (user-generated) content on the internet and its continuous growth make the selection process increasingly difficult for end-users. This abundance of content can be handled by a recommendation system that observes user preferences and assists people by offering interesting suggestions. However, present-day recommendation systems are optimized for suggesting premium content and partially lose their effectiveness when recommending user-generated content. The transitoriness of the content and the sparsity of the data matrix are two major characteristics that influence the effectiveness of the recommendation algorithm and in which premium and user-generated content systems can be distinguished. Therefore, we developed an advanced collaborative filtering algorithm which takes into account the specific characteristics of user-generated content systems. As a solution to the sparsity problem, inadequate profiles will be extended with the most likely future consumptions.
These extended profiles will increase the profile overlap probability, which will increase the number of neighbours in a collaborative filtering system. In this way, the personal suggestions are based on an enlarged group of neighbours, which makes them more precise and diverse than traditional collaborative filtering recommendations. This paper explains in detail the proposed algorithm and demonstrates the improvements on standard collaborative filtering algorithms.
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